January 27th, 2010
Attendees:
Oversight Committee Chair:
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Maryann Martone, University of California, San Diego, maryann@ncmir.ucsd.edu
Project Coordinator:
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Jyl Boline, Informed Minds Inc., Wilton Manors FL, USA, jylboline@gmail.com
Neuron Registry Task Force:
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Lead: Giorgio Ascoli, George Mason University, Fairfax VA, USA, ascoli@gmu.edu
Sean Hill, L’Ecole polytechnique fédérale de Lausanne, Switzerland,
sean.hill@epfl.ch
Gordon Shepherd, Yale University School of Medicine, New Haven, USA,
gordon.shepherd@yale.edu
Menno Witter, Norwegian University of Science and Technology, Trondheim,
Norway, menno.witter@ntnu.no
Representation and Deployment Task Force:
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Lead: Alan Ruttenberg, Science Commons, Cambridge MA, USA,
alanruttenberg@gmail.com
Mihail Bota, University of Southern California, Los Angeles, USA, mbota@usc.edu
Gully Burns, University of Southern California, Los Angeles, USA, gully@usc.edu
Alexander Diehl, Jackson Laboratory, Bar Harbor ME, USA,
adiehl@informatics.jax.org
Sarah Maynard, University of California, San Diego, USA,
smaynard@ncmir.ucsd.edu
Onard Mejino, University of Washington, Seattle, USA,
mejino@u.washington.edu
David Osumi-Sutherland, University of Cambridge, United Kingdom,
djs93@gen.cam.ac.uk
Structural Lexicon Task Force:
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Lead: David Van Essen, Washington University, St. Louis, USA,
vanessen@brainvis.wustl.edu
Rembrandt Bakker, Radboud University, Nijmegen, Netherlands,
R.Bakker@donders.ru.nl
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Stephen Larson, University of California, San Diego, USA,
slarson@ncmir.ucsd.edu
Laszlo Zaborszky, Rutgers University, Newark, USA, zaborszky@axon.rutgers.edu
Other Participants:
Digital Atlasing Infrastructure Task Force:
 Lead: Ilya Zaslavsky, University of California and San Diego Supercomputer
Center, USA, zaslavsk@sdsc.edu
 Christian Haselgrove, University of Massachusetts, Worcester, USA,
christian.haselgrove@umassmed.edu
Metadata Standards Oversight Committee:
 David Kennedy, University of Massachusetts, Worcester, USA,
david.kennedy@umassmed.edu
INCF Secretariat:
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Janis Breeze (Manager of Programs), janis.breeze@incf.org
Raphael Ritz (Scientific Officer), raphael.ritz@incf.org
Albert Burger, Bernard de Bono, Melissa Haendel, Rolf Kötter, and Nicolas Le Novere all
joined in for part of the day via teleconference.
Overview and Goals: Maryann Martone
Overview:
The goal of the INCF program on Ontologies of Neural Structures (PONS) is to promote
data exchange and integration across disciplines, species, developmental stages, and
structural scales by developing terminology standards and formal ontologies for neural
structures. A focus will be to create solutions to help resolve many of the barriers
around sharing information and data integration, which revolve around people’s use of
ill-defined terms and structures.
History:
The first Oversight Committee meeting was held in Stockholm in the fall of 2007. They
focused on general principles for developing a systematic, useful, and scientifically
appropriate framework for neuroanatomical nomenclature and created basic principles
for implementation strategies. The report from the workshop discusses the problems
arising from the use of different parcellation schemes and terminologies and highlights
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the need for a universal vocabulary for describing the structural organization of the
nervous system.
A second workshop was held in Stockholm in the fall of 2009 with a focus on building
informatics infrastructure. The scope was defined and three task forces were formed
to focus on neuronal structures, neurons, and the supporting infrastructure (which has
evolved into deployment and representation). Structural lexicon infrastructure was
developed under the umbrella of NIF for neuroscientists to enter definitions and expose
standards to humans and machines.
The goals of the current workshop are to:
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Establish necessary interactions among task forces so that we end up with a
consistent terminology structure for cross scale queries
o Neuron registry should reference structural lexicon and vice versa
Define the use cases
Refine deliverables
o Common high level ontology for mammalian anatomy for cross rodentprimate anatomy that ties together nomenclatures in common use for
rodent and primate (Doug Bowden and Laszlo Zaborszky)
o Conventions for naming of neurons and brain regions
o Definition of a standard set of properties for describing neurons and
brain regions
 Connectivity across scales
o Common strategy for representing species differences
o Set of software tools for populating lexicon and knowledge bases to be
built from lexicon
o Strategy for tying efforts to atlasing, modeling and metadata task forces
Determine who will do the work
Goal: Demonstration of products at Neuroinformatics 2010 in Kobe at the end of
August
Overview of SLTF: David Van Essen
The SLTF has been focusing on the entity pages for brain structural entities in NeuroLex.
Efforts of this group (mostly David and Stephen Larson) have revolved around improving
the NeuroLex wiki interface and creating tools for bulk uploads. David has been trying
to enter Area 25 in the primate and demonstrated the multiple difficulties that arise
because of differences of opinion and parcellation criteria. He also showed that this is
an extremely complex issue as there may be some boundaries, but more often the
brain, especially the cortex is a continuous sheet and different criteria often lead to
different parcellation schemes across the cortex. Ideally, probabilistic atlases could be
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used to help illustrate these characteristics. Whatever is created needs to be useful for
many other data types.
SLTF Goals for this meeting:
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Refine NeuroLex metadata categories, characterization
o Exemplar use cases (e.g., ‘area 25’)
o Minimal vs optimal characterizations
Build upon Scalable Brain Atlas
o Additional parcellations/atlases in SBA?
o Additional parcellations in BrainInfo, SumsDB?
o How to link NeuroLex to these resources?
Prepare for the future
o Connectome-based parcellation
o MR-based architectonics
Key Issues for Cortex:
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Multiplicity of parcellation schemes; won’t go away
o Examples - Paxinos et al (PHT00), Ferry et al. (FOAP00)
Individual variability (especially in humans)
o Need for probabilistic maps
Graded strength of areal boundaries
o Architectonics
o Connectivity gradient analyses (DI, R-fMRI)
Areas, area clusters, networks
o Modularity, graph-theoretic approaches
Overview of NRTF: Giorgio Ascoli
The goal of the NRTF is to create a resource to browse and search neuron types based
on their properties and properties based on the neuron types in which they are found.
Giorgio gave examples of use cases and discussed how the lack of consensus in this field
presents one of the largest hurdles to their goals. Fairly recently, there has been a
significant advancement with the creation of the Petilla terminology, a stepping stone
within a larger classification effort that began in 2004. The group did not move on any
classifications, but agreed on a list of terms for properties that are crucial for the
description of neurons.
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In addition, Giorgio discussed some of the challenges, both scientific and technical, of
trying to create a resource such as this and in conjunction, an interface that gathers
information from the domain experts.
So far NRTF members have focused on investigating multiple domain-expert interfaces
rather than NeuroLex, since that interface isn’t ideal for what they want to do.
Whatever is used must be compatible and integrated with the backbone of what’s in
NeuroLex.
They’re tackling the problem in a pragmatic way, by narrowing their focus and linking to
the SLTF for location and chemoarchitecture and leaving the ontology organization to
the ontology experts.
Proposed timeline:
Phase I: June-Dec 2009
 Refining scope, agreeing on properties, designing curator interface
Phase II: Jan 2010-August 2010
 Testing curator interface, seed populating Registry, designing user interface
Phase III: Sept-March 2011
 Testing user interface, devising system of ongoing curation, release of beta
Neuron Registry, and write report for publication
Goal for this meeting is an operational decision about how to proceed over the next ~9
months and to start putting together other longer-term goals and actions.
Overview of RDTF: Alan Ruttenberg
The group has focused on starting to collect potential relations (how the different things
are connected to each other, e.g. located in). David Osumi-Sutherland has started
collating the multiple relations from different efforts (many without definitions) and the
group is starting to look at how to navigate across them. They’ve started scoping what
is needed as supporting representation “high level entities” (initiated by Gully Burns),
and started reviewing and further formalization of BAMS.
During this meeting, he’d like to work on trying to represent neurons and brain regions
together by evaluating existing work and recording what doesn’t work. Also he’d like to
ensure the group understands the formal representation task and to distinguish this
from the user interface. He’d like to ensure that people understand each other.
They would like to do some initial formalization which will result in an OWL file. and
Review and set goals for NeuroLex as it is a main component of user interface.
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Representation Task:
Be clear about what is being represented, communicate with clarity and good
documentation, reuse and integrate resources and ensure that we can express and
answer queries precisely.
User Interface Task:
This is used to allow domain experts to effectively communicate their knowledge, and
needs to be expressed in formal representations. These two views need to be kept in
sync. Some of this depends on what has already been developed, but if we are defining
the ontology, we can create a new one that is very controlled and specific (formally
defined in such a way that future people can follow the same path).
What is a Type:
This session was primarily to help the Representation and Deployment TF get up to
speed on understanding associated representation issues, primarily on what is the
thinking behind the different types of criteria used for classifying things.
Cell Classifications:
Giorgio discussed how different people use different criteria to “classify” cells. He
showed a table (created in his lab on the basis of extensive literature mining) that gives
an example of how neurons in the hippocampus can be “classified” by the patterns of
distinct subregional layers invaded by axons and dendrites. This doesn’t include the
physiology or chemistry of the cell. He emphasized that people will use different
criteria, which leads to very different classification results. The Registry can help by
showing possible alternatives with an observed subset of properties. Moreover, that the
Registry would also indicate when something isn’t known.
We need to put an information structure in place to address these issues. We need to
ensure that we have use cases in place that guide the development of this
infrastructure.
Structure Parcellations:
David discussed how people use parcellation as a way to get a handle on function and
connectivity. Connections are not binary, as they are usually described, but may be
thought of more as a “probability.” The range in connective strengths is a factor of 105
or 106. Even many of the “weak pathways” are very consistent across organisms, which
strongly suggests an important functional role. When we think about our descriptions
of cell properties and connections, we should keep in mind that the range of
“connection strengths” will be tremendously diverse, and have some ways to support
that information if we can.
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He also touched again on the issue of instances the different parcellations set by
different people. All these areas may not have equal value and robustness, and this
comes back to the idea that the cortex may be more the functional components of a
network than individual parcellations.
Again the evidence for gradients within a parcel shouldn’t be ignored and that what may
be needed to characterize domains throughout the brain will include a mixture of
parcellation and location within a large parcel. This of course ties strongly to what the
atlasing group is doing so we should think about how we can engage with them as we
move forward.
There was a brief discussion about current technology that may aid in dealing with the
issue of granularity of descriptions that justify parcellations, in order to identify and
encode the chunks of publication that relate to a particular area. Gully’s group is
creating LATISI (literature annotation tool from the information sciences). A user can
download a pdf, render it and interact with the text. In the future, there would be a
way to interact with the database.
OBO (Open Biomedical Ontologies) Foundry Approach: Alan
Alan discussed the OBO Foundry approach, which includes a subset of ontologies whose
developers have agreed in advance to accept a common set of principles reflecting best
practice in ontology development designed to ensure:
 tight connection to the biomedical basic sciences
 compatibility
 interoperability, common relations
 formal robustness
 support for logic-based reasoning
Their principles for ontology development are built on collaboration and consensus
using clear communication, documentation, attribution, and curation. Results should
enable understanding of how ontologies are developed and the relations across
different ontologies. They should also support reasoning, so you get out more than
what is put into the ontology.
Creating ontologies and their components:
Upper level distinctions:
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Continuants (Things)
o Independent Continuants, exist independently: Cells, Molecules
o Dependent Continuants, exist in relation to something else
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
 Qualities: Shape, Mass, Reflectivity (subject of PATO)
 Realizables: Kinase function, IRB member role, being a drug
o Generically dependent continuants (Information)
Occurrents (Processes)
o Having difficulty breathing
o A metabolic reaction
o A mass spectroscopy run
“Instances” are defined as:
• Objects (particulars, independent continuants) “fully present at every time when
it exists”
– one coho salmon
– The National Oceanic and Atmospheric Administration
– one person
– one notebook
• Properties (dependent continuants) “fully present at every time when it exists”
– The ability of a passive integrated transponder tag to respond to a radio
signal (a function)
– NOAA Fisheries Service charge to review locally prepared salmon
recovery plans (a role)
– one salmon’s fork length (changes over time)
– the measured value of one salmon’s fork length in millimeter
• Processes-“takes place (unfolds) over a period of time”
– A salmon swimming up a salmon ladder
– Member of the NOAA Fisheries Service preparing advice to augment a
salmon recovery plan (realizing their role)
– A PTT responding to a radio signal (executing its function)
– A fisheries staff member weighing a salmon
Relations between instances:
• trap17 located_in xxx river
• salmon23 has_quality {fork length of 12.2 mm} now
• attenna78 has_function PTT detection
• John Samuels has_role Biostatistian
Classes:
• Those entities that are alike in some way
• Mostly expressible as the relationships that their instances have to other
instances
• A PTT Antenna is an antenna that has function PTT detection function
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– ALL instances of PTT Antenna are an instance of Antenna that
has_function SOME instance of PTT detection function
There was a discussion about how deep we will go. We must define the situation where
a given term is used so a user can evaluate effectively whether or not it fits their needs.
In practice, when there’s a dispute, the process goes to collaborative debate and
sometimes these terms get left behind and two different terms get created. We want to
get people to use what exists already and if something doesn’t exist to get them to
define it in terms of things that already exist.
We don’t have to get it right the first time, we can modify it and it will get better over
time. We are not building a dictionary; the definitions we develop will fit our needs and
won’t satisfy everyone. There needs to be an interface tool that allows people to see
the definitions and classes and has the ability to issue queries.
Original Requirements of Steering Committee: Maryann
Maryann reviewed the original Steering Committee requirements about the
development of the PONS infrastructure with the purpose of seeing what we have
achieved and to get a better handle on what we still need to do.
Initial Requirements were as Follows:
Infrastructure:
• Infrastructure should handle information ready to go into a textbook as well as
areas still under debate (controversy should be allowed and made accessible)
• Must be able to handle versioning
• The first Neuropedia (now Neurolex) is unlikely to allow query across a lot of
different sources, but this would be desirable in future implementations
• Need to define links with space and atlases. Tying annotations to an atlas is a
registration issue and beyond dealing with just images. Work with the Atlasing
Task Force on this.
• Need to define how to link this to electrophysiology, possibly link an
electrophysiology database via the ontology and atlasing framework.
• Need to provide services for tool access in the future (e.g., atlasing tools)
• Authority
• Easy to use interface for trained users to add information (e.g. workshops for the
dedicated domain experts)
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Usability:
• Whatever is created needs to be market-tested and modified to fit needs
• Needs to be tested by tool-builders accessing services
Metadata Structure for the Lexicon:
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There was a lot of discussion of initial metadata structure for the structural
lexicon entries and a template was created that includes items such as the
species, partitioning scheme, etc. This has evolved over time and is used for
inputting information into the NeuroLex wiki.
Also, there was a fair amount of discussion over the term “metadata,” as it
seems to be a loaded term and may or may not be appropriate for this group
and its goals.
There was a lot of discussion around methods for building this for people vs. text
mining. Distinguishing different synonyms with the context of IDs are useful, but there
are other methods to further define these individual terms. We need to be careful
about putting constraints on our ontologies for text mining. Our primary goal should be
around defining terms well, and that text miners can figure out that there are
synonyms, etc, but not to build for that particular purpose.
More Requirements were Developed over Time and Include:
• Access to synonyms
• Link the definitions to an atlas that visually displays the definition (being done
now)
• In the future, if someone links to a space in an atlas, all the terms for that space
should be shown-these might be ranked with a few “INCF-stamped” registries
• History of term
• Problems with the term
• Link back to figure in published literature
o INCF to negotiate with publishers for rights
• Link to a higher order brain structure
o Classical anatomy
o Brain Info structure
• Citations for every relationship
The goal is to review these requirements and come back with recommendations for
improvements, or if we think something can’t or shouldn’t be done.
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Scalable Brain Atlas (SBA): Rembrandt Bakker
Rembrandt started with a review of CoCoMac-Paxinos 3D viewer (precursor to the
Scalable Brain Atlas) and how it didn’t quite fit the needs they were trying to fill. They
wanted a tool that didn’t have to be downloaded and would be easy to use. To meet
these needs, they built the Scalable Brain Atlas (SBA, http://scalablebrainatlas.incf.org/).
SBA is web-based, and they can put in any atlas template that supports scalable vector
graphics (SVG). They also plan to provide services to other websites and databases. It’s
currently being used as an image service to NeuroLex for structures. They will also
supply other services such as brain region coordinates in XML and a connectivity display
for CoCoMac.
He gave an example of viewing the Paxinos macaque atlas in SBA, where it’s possible to
see the structure in pseudo-3D, or 2D plus. It isn’t complete yet and was hand-copied
from the paper atlas, so they haven’t had too many copyright complaints. They may
wish to go with public domain atlases such as the Allen Brain Atlas.
Discussion issues:
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Copyright:
o We’ll need to have people look into it for the different atlases
o May be able to get around by modifying them enough (e.g. apply
automated parsing algorithms
We need something else as a method of recording location:
o It should connect to high resolution datasets
o It should be possible to “paint” locations on them (more visual rather
than names)
May be worth trying to put Menno’s hippocampus atlas into this format
NeuroLex Overview and Tutorial: Stephen Larson
Since the oversight meeting reviewed by Maryann, the San Diego group has moved
forward in creating a wiki tool that fills many of these requirements called NeuroLex,
http://neurolex.org/wiki. It is sponsored by NIF and INCF and is meant to allow people
to enter neuroscience terms.
Much of the content of NeuroLex can be browsed from the main page under major
categories, arranged as hierarchies or as tables. Note that brain partonomies for a
generalized mammalian brain are accessible from the main page and at
http://neurolex.org/wiki/Brain_Partonomy_(general_mammalian). There are also quick
links on the main page to add new content. Each “type” of new entry has a different set
of fields that can be filled in.
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Given the way these are built as “blocks” you can create different “views” of the data
based on the query. In addition, you can upload bulk content with an excel template
developed with David Van Essen. However, at this point these need to be done with the
help of a curator to help prevent problems.
Cell Ontology Overview: Alex Diehl
He gave a brief presentation to illustrate the power of using different ontologies to help
build up different views of the information. By using the relationships when cells are
given definitions and described with properties, you can create more powerful linkages
that weren’t necessarily specifically defined to begin with.
For neurons, there are a set of properties that are required to define them. If there are
8 ontologies, they express these properties 8 different ways. But if there is a core set,
we can focus on those properties.
Discussion Items:
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We should choose the properties, relationships, etc. that we want to focus on
and ensure there is a page in NeuroLex that explains them. The NRTF will work
with the RaD TF on what they need to express. SLTF will also focus on a core for
properties and relationships so people can build from them. The ontologists will
be responsible for putting them into a formal structure.
As we move forward we need to realize that user interface and underlying
infrastructure is not the same thing. However, often both need to be developed
in conjunction.
The RaD TF has started compiling a set of existing relationships and terms from
different efforts. The table is huge and ungainly at the moment, but it lists the
existing properties that different groups have used, although different people
use them slightly different. They are in the process of pulling out some essential
features from this and can share their findings with the other groups soon.
The plan is to go through specific examples tomorrow and determine how to
create representations.
A suggestion for a potential deliverable for Kobe was put forward by Gully, to
have a combined representation of connectivity in the hippocampus between
individual cell types and areas and capable of handling two levels (structures and
cells) and also capable of expressing lineage information.
o We may wish to start by taking advantage of the connectivity information
from Menno and the work in progress as demonstrated by Giorgio.
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
Reoccurring themes throughout the day:
o We will need to work with the Metadata Program on these issues
o We need open-access to journals and if not, we at least need some sort
of solution for some images
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Thursday January 28:
Groups broke into the Structural Lexicon and Neuron Registry Task Force core groups
and members of the Representation and Deployment group split between these two.
Neurons and Properties:
Attendees:
Jyl Boline
NRTF:
Menno Whitter, Gordon Shepherd, Sean Hill, Giorgio Ascoli
RDTF:
Alex Diehl, David Osumi-Sutherland, Stephen Larson, Bernard De Bono (joined by
teleconference for part of the day)
Overview:
The group started with an overview of the issues discussed yesterday followed by a
review of an interface Giorgio has been working on for the Neuron Registry Task Force
and Menno’s new Rat Brain Workbench.
Discussion Topics:
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Goal is to give domain experts a chance to enter information in a flexible manner
and in a way that makes sense to them.
Ensure we define relations that people will use to input information.
Question arose if people will be entering single instances of neurons. Instead it
seems that people will be putting in information from publications, which is
more of a category that has been characterized in a paper.
Can use lists of properties to aid us in checking for identical entries.
Is there a minimal or necessary set of properties that every neuron must have in
order to be identified? Or can any neuron be entered as long as it has proper
references?
Classification will be the last thing that happens and there will be disagreement.
Keep in mind the different viewpoints of ontologists vs. domain experts: we can
accommodate both as often as possible, but focus on the domain experts when
we can’t.
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Focus on Properties:
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Petilla contains a list of features, and the group is working on common
definitions of properties, it will be an iterative process.
Use “query” as an aid in quality control, it can alert us if things are not right.
What level of granularity for description is required? Using the relation
“part_of,” we should give as much detail as possible.
How to deal with properties (such as fast spiking) that should have a range. A
potential option is to have an interface (e.g. Giorgio’s might allow this) where
the property has to be defined first and then values can be put in.
o Any properties that are added also need to have the option to have
publications and curator attached.
Giorgio’s Neuron Registry Interface Prototype:
This interface has hierarchical ordered folders with drop-down menus. The folders can
function as placeholders (not required to be populated). To create a new type of cell,
(names are irrelevant because the name can be edited later), start attaching and
entering information into the properties that describe that cell. However, an in-depth
discussion on properties of properties (see below), indicated this may not be able to
accommodate the desired data structure, thus Giorgio would like to get more
information on this so he can modify the interface accordingly.
Compare this to Cell Ontology entries (Alex Diehl): for the Cell Ontology, people give
textual information and he has a curator that combines the information in the ontology.
Discussion-State or Conditions of Experiment:
There was a great deal of discussion about how a neurons often have properties that
itself has properties (e.g. firing rate) that depend on “state” or experimental details (e.g.
species, age, behavioral conditions etc.). How much of this information is needed and
how specific should it be? There was also discussion about what these types of
properties are called, some call them metadata, others annotation, and others data.
This seems to be an especially important issue in the case of physiology. Gordon uses
models, which forces specific information about each property and the physiological
expression. It simulates some specific properties such as bursting and regular firing.
Discussion-Necessary and Sufficient:
There was a great deal of discussion around whether the properties corresponding to a
neuron type in the Registry should (each) be necessary and (all together) sufficient for
our definitions and for classifying neurons, especially since there will always be edge
cases. An example is the pyramidal cell, which is usually associated with a soma shape;
however, there are pyramidal cells that fit the definition without having a pyramidal
soma shape.
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What is considered necessary to define a cell is for it to meet the criteria of a complete
set of properties (which will vary depending on a cell). What is sufficient to define a cell
type is the set of minimum properties (matching the criteria for that cell type), which
vary from type to type.
Rodent Brain Workbench: Menno Witter
Menno gave an overview and showed their new web-based interface that links to the
rat hippocampus from the rat brain workbench:
 It is still under development so not publicly available yet.
 It contains a repository of rat brain sections (focus on the hippocampus,
entorhinal, and perirhinal area) in all three planes.
 They currently have two markers but other markers could be added.
 Segmentations are contained in an overlay.
 Descriptions of the areas (including cell layers) are linked to the names.
 Text describes a clicked area and its closest relationships in the brain.
 They don’t include definition of the cells in this interface but cell types are
mentioned in the text. It would be great to cross-link to the neuron information
once it’s available.
 Next aim is to get the 3D properties working.
 Linkage to wiring information:
o Colors are linked to another web-site that contains connectional data,
cell layers and topology.
o People can use this site to build a routing diagram.
o This is for rat, but they plan to create one for the monkey and rat
retrosplenial cortex.
o Their code should be general enough for other areas of the brain.
o This is built on a reference manager database (~160 papers).
Related Discussions:
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Coordinating and syncing this information to NeuroLex is further down the road
but high on the list of next steps.
These area descriptions may be useful for the SLTF.
This could act as a scaffold for many of the entries of the neuron registry TF.
It would be good to integrate this with the atlasing effort, although it might be a
little early (since that focus is on the whole brain and mouse at this point).
It would be great to share some of this via services (XML for boundaries and the
access to the annotation page).
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Properties Structure:
A fairly lengthy discussion was had about properties and the organization of the data
structure. What is a property and how do we determine what properties are assigned
to a neuron? This led to a discussion of a relation and its value. A relation was
described as how two instances relate to each other, for instance:
A neuron:
 Has shape
 Has location
 Has terminus
 Has role
 Has neurotransmitter
 Develops from
 Has orientation
 Has size
 Has firing pattern
 Has afferent
 Has efferent
 Part of
 Participates in (e.g. awake, behaving)
A relationship was described as how an instance has relation to a value. Example: basal
dendrites of CA1 pyramidal cells has terminus in area X
At this point, the group broke into smaller ones. One group worked on graphically
describing an example CA1 pyramidal cell with limited properties. Another group
worked on trying to find what might be a set of core defining properties in order to
ensure definitions are developed and vetted for them.
Structural Lexicon and Ontology Development
Attendees:
Maryann Martone, Janis Breeze
SLTF:
David Van Essen, Rembrandt Bakker, Stephen Larson, Laszlo Zaborszky
RDTF:
Alan Ruttenberg, Mihail Bota, Gully Burns, Sarah Maynard, Onard Mejino
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Overview:
Maryann presented the 3 scientist ‘use cases’ that help define the goals of the SLTF and
the upcoming demos in Kobe:
1. If I have an electrophysiology/fMRI study in primates that shows activation in
areas of cortex, and I want to compare these results with genes expressed in the
Allen Brain atlas, then I will need to map across primate cortex to mouse cortex.
What are the underlying shared structures that allow me to make this cross-atlas
and cross-species comparison?
2. If I’m creating a gene expression atlas in mouse brain, and would like a simple
and computable hierarchy of high-level structures, where can I find consistent
definitions of high-level structures across species?
3. If I have created an atlas with its own parcellation scheme, how can I incorporate
an existing nomenclature? Can I represent my atlas in the Scalable Brain Atlas
(SBA)?
More specifically such a user might be a:
1. Scientist looking for data, e.g. genes in cerebral cortex
2. Anatomist with a new atlas
3. Molecular biologist with lots of money to create a mouse brain-protein
expression atlas
4. Computer developer
Regarding the various parcellation schemes that already exist or will be created: We
should encourage all atlases to have representation in the SBA, which ties to Neurolex.
Perhaps a future INCF service could be a registration process for new atlases.
Review of current resources for neuro-ontologies: FMA and BAMS
Review of FMA (Onard)
Onard reviewed the principles underlying Basic Formal Ontology (BFO) and the
Foundational Model of Anatomy (FMA) Ontology (Powerpoint available).
A first question for our group: How high do we want to define the hierarchy? Once the
higher-level classes are defined, then ontologists will determine the underlying
structure, independence, etc.
The FMA is essentially a top-down approach: you start at the root, determine which
anatomical entity is the root class, and follow with a “single inheritance class hierarchy”;
things get grouped together based on common properties.
This process allows for a 1/2/3-dimensional representation of the brain. All anatomical
entities are either material or immaterial:
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all entities must have dimension
material entities must have mass
immaterial entities (“spaces”) don’t have mass – but they’re still 3-D
inheritance of properties is propagated
“portion/substance” does not inherit 3-D space
“cardinal” structures refer to things like head, trunk, limbs
Review of “fiat” boundaries versus physical/bona fide boundaries:
 fiat boundaries can be anchored or floating (e.g., dependent on a surgeon’s eye)
 fiat boundaries are typical of neuroanatomy
Example from rodent barrel cortex: There’s nothing on the surface of the brain that
indicates barrel cortex, but staining reveals clear zones. Filling cells show that while
some respect boundaries, others don’t. Staining reveals some cell organization, but
other cells can move freely between zones. What type of boundary does this suggest?
Probably bona fide as there is a clear zone, depending on the level of granularity (i.e.
will see bona fide boundaries at microscopic resolution, whereas gross divisions tend to
be fiat).
There are different kinds of parts: regional (cell body of neuron) and constitutional
(plasma membrane of neuron):
 Regional parts of brain: forebrain, midbrain, hindbrain (like geographic states)
 Constitutional parts: neural tissue, vasculature, ventricular system (like
mountains, lakes, trees)
A common problem with nervous system cells is that they don’t respect the
divisions/territories we would like to define, e.g., amygdala (Helmer vs. Swanson),
which has changed because of observations from staining. Laszlo explained that the
classic definition of amygdala was “extended” because staining revealed continuity with
surrounding areas. Another example is Substantia inominata – named because
classically it was not known what it should belong to; however, staining now shows
chemo-architectural commonalities with surrounding structures (striatum, amygdala).
Different partitioning schemes also exist based on the scientist/practitioner’s approach:
histological, surgical, anatomical/morphological, and the challenge is how to correlate
the three. Other anatomic entities have a framework for doing this (e.g., prostate,
kidney, etc), but their partitioning is simpler.
For a particular definition, you can add as many properties as you want. So neuron has
multiple definitions: it is enucleated, it is a neural cell, it has a cell body, etc. These
definitions deal only with structure (not function). Later, one can perhaps include
connectivity, constituent molecules rather than molecules that are expressed, location,
etc.
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Surgeons might say “parietal lobe” and by that term include subcortical structures as
well, or at least the white matter; neuroanatomists would use that term to mean only
cortex. To solve the problem of differing use of the same term within the community,
we give the terms suffixes and therefore a unique label (this is analogous to Mihail’s
approach, in which every term is associated with a nomenclature). Thus FMA ensures
that each term is qualified, but can also extended. This leads to an important
clarification:
 In FMA: if 2 names refer to same thing, they are still one entity.
 In BAMS: if 2 names refer to the same thing, they are still considered separate
entities
This requires careful use of synonyms, cross-linking, etc.
FMA is currently working with RadLex to enhance the neuronatomy content of Radlex to
improve the annotations they use for fMRI. Need to reconcile all the different
parcellation and/or naming schemes, whether based on topographic or
cytoarchitectonic approaches (e.g., for human brain there is Talairach vs. Freesurfer vs.
Neurolex).
Review of BAMS (Mihail)
The constructing principle is that any term/concept is always defined by the reference in
which it is published. Every term is associated with a publication. For brain regions, we
don’t know which one label is best. We use a single nomenclature for anchors (though
there are many out there). Nomenclatures are species-specific. A nomenclature can be a
single term, or can refer to an entire region or species.
The anchor here is the “BAMS Neuroanatomic Ontology” which is in the rat domain
(Swanson 1988), but upper-level brain regions can be applied to any mammalian
species. This is general enough in definition to apply to humans. At the upper level, we
have organism and cell.
(Mihail to talk to editors for Swanson about getting atlas definitions online to
incorporate into SBA; legal issues, etc.)
Nomenclatures are not “fixed” – they can be merged, expanded, revised, etc. Gene
expression data is available across nomenclatures. Questions to ask: if we wish to
standardize a nomenclature:
 Should the terms reflect the way that people would organize brain regions?
OR
 Should they be organized the way an anatomist would prefer to see it? (e.g.
midbrain and hindbrain as part of brain stem)
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Alan: Terms are always ambiguous. We want to accommodate different nomenclatures
as the major ontologists are doing. The strength of BAMS is the ability to grab the
citation easily; with FMA it’s more difficult to get back to the citation.
Best practice that INCF should adopt: for any representation that is presented, people
should be able to disagree.
How do users define striatum for usability/experimentation/etc? Currently Neurolex
says “Striatum of X (2000)” and ”Striatum of Y (2002)”. We can have an overall, classical
“striatum” under which all striatums are grouped. Striatum mammal can be a term that
refers to this piece of brain shared by all mammals.
What about terms existing in multiple nomenclatures?
Alan: When things are identical, they are synonyms. Every term must refer to some
piece of brain. If terms have identical definitions, then that term becomes one, and
they point to two different papers or nomenclatures. If they are not identical, they will
be striatum A and striatum B, and each will refer to its respective source. If possible, we
will link out pictures to accompany these terms.
Example of the FMA definitions of Thalamus and Brain Stem are shown below.
Definitions can be modified, expanded, and include previous definitions, always
referencing the source.
Thalamus, rat
Thalamus, SW 92 (identical to SW 98)
Thalamus, SW 98
Thalamus, SW 04
(e.g., “thalamus as defined by Swanson in 2004
publication”)
Brain Stem
Brain Stem, SW 92
Brain Stem, SW 98 (part of SW 04)
Brain Stem, SW 04 (part of SW 98)
How do we get to a high-level shared parcellation if we have different fundamental
definitions of brain stem? Hierarchy is a strict consequence of definition. (DVE:
Hierarchical relationships get problematic when Swanson is putting thalamus in the
brainstem.)
For this nomenclature, we get Thalamus-Swanson-1998, which is part of BrainstemSwanson-1998. (Note the definition of thalamus from BMA is entirely structural,
whereas the Swanson definition is primarily developmental.) We could create Thalamus,
consensus to include all of SW 92 98 04.
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Plan before Kobe:
Each expert TF member gives his/her definition of a structure, then Stephen and
Maryann deal with the Neurolex side of mapping it across rat/mouse/monkey/human.
We should start with the cerebral cortex of rat/primate/human/macaque (perhaps
getting Rembrandt’s help via Cocomac).
For cross-species definitions, they will be much more generic, e.g. for Cerebral Cortex,
one can’t say that “it contains lobes” since that statement is not true of all species.
Maryann: As a starting point, we can use Doug Bowden’s “Rosetta Stone” list derived
from BrainInfo: a collection of terms that people have commonly used (available at
http://braininfo.rprc.washington.edu/OtherModels.aspx?requestID=3071&questID=21&
pTerm=NeuroLex+Mammalian+Brain). We will take that list of structures, and create
consensus definitions for all of them, starting with pan-mammalian structures.
Underneath pan-mammalian will be rodent/primate versions. The technical back-end of
this nomenclature will provide translation among branches and among correlated
structures across species.
Clarification: Consensus structures are those that people “know” what they mean. (An
example of a non-consensus structure is ‘lentiform nucleus’ (it’s not a “primary” term,
as it refers to a bunch of structures.)
To do (Stephen): Create disambiguation pages for Neurolex (similar to Wikipedia).
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EXERCISES IN DEFINING TERMS AND PARTS
Hippocampus
(Group joined by Menno Witter)
How to define hippocampus? There are many possible definitions:
 Neurolex: http://neurolex.org/wiki/Category:Hippocampus
 Gully: Use gross anatomy based on how it looks on Nissl stain.
 DVE: “Architectonically distinct part that includes sheet-like region that includes
CA1-CA3 or CA4, adjoined by the subiculum (CA1) and termining in dentate
gyrus.”
Then describe parts of hippocampus. There are 3 layers:
 Molecular layer (outer layer)
 Cell layer (Pyramidal in CA1)
 Polymorph layer (Oriens in CA1 and CA2)
Then break down further….
In-depth review of BAMS
Using as an example the Bed nuclei of the stria terminalis (BST) in rat: Bed nuclei of the
stria terminalis (can take a while to load and/or does not always load).
(Noted that neurolex definitions currently lacking
http://neurolex.org/wiki/Category:Stria_terminalis (currently defined via Wikipedia)
http://neurolex.org/wiki/Category:Nucleus_of_stria_terminalis)
Start with first citation: seems to be Johnston 1923 (Journal of Comparative Neurology)
– he called it the bed of stria terminalus) can also be called the special nucleus of stria
terminalus. This was followed by Guroljian 1925 (Journal of Comparative Neurology)
which provided topographical data. Until 1960, there was considered to be a single BST
(bn.st). Then in 1963, Blzier divided the structure into BST a, b, c, d, e (based on
cytoarchitecture data: Nissl stains and landmarks).
Two BST paradigms then emerged:
 divide BST into posterior and anterior
 divide BST into medial and lateral
In 1987, Bayer looked at BST from the point of ontogenesis and recognized anterior and
posterior parts. This is the current definition of BST.
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January 29th
Attendees:
All from the 27-28th, in addition:
 Ilya Zaslavsky, University of California, San Diego, CA, USA, zaslavsk@sdsc.edu
 David Kennedy, University of Massachusetts, Worcester, MA, USA,
david.kennedy@umassmed.edu
 Albert Burger and Clif Saper joined for part of the day by phone
The day began with a continuation of the small group working sessions that started on
the 28th.
Discussion of Structural Delineation Issues: Laszlo Zaborszky
Laszlo gave an example of an important issue moving forward. In the basal forebrain, it
is difficult to create boundaries using the cytoarchitectural or other structural features.
They found that four different cell types in this area display a general pattern of highdensity clusters. The other three cell types form twisted bands along a central dense
core of cholinergic cells traversing the traditionally defined basal forebrain regions.
A representation of the space that the cell types occupy is X…but the space covers
multiple regions. We need a general representation of what space this occupies in the
brain. These cell populations make odd shapes in the brain, but it doesn’t necessarily
correspond with the structures that people delineate in the brain.
This exemplifies the difference between cell types and cell populations (or cluster), here
you’re talking about the instance of the type as a cell population vs. the instance of the
type as a cell, which are modeled in a different way. A brain region is not the same as
the space of a region.
This can be described with the ontologies using “scattered aggregates”, we can create
concentrations and densities, and describe containers in which these aggregates sit.
Wrap up of Working Sessions:
NRTF summary (Giorgio):
Review of major topics covered by the group during their working sessions:
 Ensure that information can be easily accessed, visualized, and entered.
 Discussed what is necessary and what is a sufficient set of properties that every
neuron must have in order to be identified, e.g. we discussed pyramidal cells
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that don’t actually have a pyramidal shaped soma, in which case, we either
create 2 subclasses, or remove this property if not actually necessary to classify
those cells.
Discussed what might be the difference between informatics vs. domain expert
point of view and issues. Work on common definitions of properties (will be
iterative).
Reviewed potential Neuron Registry interface to define properties and neurons.
There needs to be a protocol put into place that links tools like this to NeuroLex.
Clarified 5 components for assigning characteristics: relation, value, part of the
neuron that gains that relation, a reference (pub ID), and a free-text optional
note by the curating domain expert.
Created a graph (below) for visualization and to help us better understand each
other. A few points came out of this exercise:
o A dedicated interface is needed to facilitate entry and representation of
this kind of structure.
o We need to assign references and authorship to each and every property
separately.
o We will have to investigate OWL to see if it can accommodate this.
SLTF summary (Maryann):
Review of major topics covered by the group during their working sessions:
 Discussed some use case scenarios of who might want to use this, including
people searching for information, people that want to annotate data, people
that want to adhere to the structure label standards of this group, and computer
scientists might need services
 Reviewed FMA model (Onard): human anatomy with a top-down approach
 Reviewed BAMS model (Mihai): rodent, deals with a lot of different
nomenclatures and defines spatial relations between them. Based on Larry
Swanson’s view of a brain heierarchy (some of the other anatomists don’t
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necessarily agree with it). Led to a lengthy discussion and some disagreement
about what’s the best way to create a consensus view, or if it’s even possible.
They decided there needs to be disambiguation information, e.g. tell people that
“hippocampus” is used x different ways and recommendations for how to use it
Decided there can still be a “rosetta stone” for anatomy, with structures most
people can agree on, and include relations to other hierarchies/nomenclatures.
o Worked on list:
 David VE: cerebral cortex
 Laszlo: brainstem and basal forebrain
o BrainInfo is a disambiguation resource, shows how many times structures
come up in the different hierarchies, no computable structures.
o BAMS has a lot of work already, especially at the level of nomenclatures
o FMA has a model that can handle some of the representations we need
o Use these resources together to tackle this issue
Additional SLTF Summary (David VE):
He’s been working with Stephen and Rembrandt off-line, with the goal of creating a
mapping of parcellation schemes into NeuroLex with as much description as possible
along with linkage from NeuroLex to extended Scalable Brain Atlas and other resources.
Example:
The description of Area 25 in NeuroLex at
http://neurolex.org/wiki/Category:Ongur,_Price,_and_Ferry_(2003)_area_25 could
include a link that goes directly to the SumsDB, which can launch WebCaret, showing
the area in an average MRI volume (surface or volume) in both human and monkey.
Secondly, they’d like to get some of the Sums DB parcellation schemes (human,
macaque, and possibly rat and mouse) into SBA. They may also be able to map mouse
to WHS, interact with the atlasing group, which was followed by a general discussion of
how to interact with the atlasing group. In summary, the needs of PONS must be
prioritized for the atlasing group (discussion to follow this meeting).
Planning:
Kobe Demonstration:
Mammalian hippocampus representation. Using statements of cellular connectivity,
show that we can go to regional connectivity and vice a versa.
 Need formalization of the structures (Menno), cells (Giorgio), and logical
formalizations (RD TF)
 Need to follow up with RD TF on other pieces that are needed to reach this goal
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Do this first for a set number of brain structures and cells, then we can write best
practices for this process (start after Kobe, although we may be able to start parts).
Workplan:
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Create specific use cases
Create detailed representation of hippocampus structures and cells (Menno and
Giorgio)
Logical formalizations set up by RD TF
Put it into a formal model (OWL)
Implement in FMA
Show cross scale reasoning
Show cross species query
Show how INCF can recommend a more clear nomenclature
See what can go into NeuroLex and how
Identify what still needs to be required and what needs to be displayed
Demo 2:
Registering a new nomenclature to Neurolex.
Action Items:
NRTF Action items:
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For Kobe demonstration:
o Create a set of entity lists that NRTF needs to complete this demo
o Contribute definitions that the group already has in difference resources
o Create specific queries that might be used for this demonstration, also
generate additional queries in areas outside of hippocampus to ensure
what we build isn’t too focused on one area
Have electrophysiologists review our interfaces (Neurolex, Giorgio’s, and Paul’s)
to see if it is possible for them to describe electrophysiology characteristics.
Examine OWL for limitations
Review relevant ontologies in a systematic manner
Review current terms in Giorgio’s and NeuroLex interface to see how an
ontologist would structure them. Examine property list and determine how it
can be divided into properties and values and how it relates to ontologies
Look at definitions in sense lab (Giorgio) and put them into GO
Compile existing and missing textual definitions of locations for the entorhinalhippocampal complex (notably, layers) from Menno’s interface and determine
how to sync with NeuroLex in the future (Menno, send to Jyl & Giorgio for
distribution to NeuroLex and other PONs)
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Convergence on a set of core properties that are likely to be commonly
considered “necessary and sufficient” to describe a cell e.g. a few use cases
suggested 3-4 descriptors would do, likely including location and/or shape of
some combination of soma, dendrites, and axon, as well as the
neurotransmitter, possibly the cell marker, and firing pattern was good to have
but did necessarily seem to be a defining characteristic for these use cases.
Annotation of neurons:
o Menno: an entorhinal and a presubicular neuron
o Sean: S1 (non-barrel) Martinotti cell and S1 (non-barrel) nested basket
cell
o Gordon: two (potentially more) undecided cells
o Complete annotation of 8 more cells from remaining NRTF members
(NRTF members) [mid-April]
o People must also define properties if they don’t already exist
Ensure properties we define and create is handed over to PATO
Work with the cell graph and convert into OWL (David OS)
Complete the Excel template with relations, values, property structure, and one
plausible-looking example and circulate to Jyl, Menno, Sean, Gordon, and David
OS (Giorgio)
Map relations and values to existing unique identifiers in NeuroLex or exisiting
ontologies, and note the missing ones (David OS) [mid-February]
Write up NRTF operating principles we agreed on and circulate to TF (Giorgio)
Implement a working interface for use by task force members and test it with 14
above examples (Giorgio) [July]
Ensure interoperability of wiki, spreadsheet, and curator interface with model
formalism, access to current ontology terms, definitions, and synonyms (Giorgio,
Stephen, RDTF) [July]
SLTF Action Items:
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For Kobe demonstration:
o Create a set of entity lists that SLTF needs to complete this demo
o Contribute definitions that the group already has in difference resources
o Create specific queries that might be used for this demonstration, also
generate additional queries in areas outside of hippocampus to ensure
what we build isn’t too focused on one area
List of consensus structures for mammalian brains (Laszlo, Maryann and Doug)
o March 31st: Review Doug’s Rosetta Stone structures and proposed
hierarchy
o Identify which ones are consensus between rodent and primate
o Add the appropriate structure to the Neurolex
WHS structures delineated with consensus structures (Laszlo)
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o Protocol one day meeting ~April (Laszlo, Doug, Maryann, Seth, Jyl)
o Subset of x number of structures ready by Kobe
o Finish structure delineations within a year
Add all rodent brain structures in BAMS to Neurolex Wiki (Stephen and Mihail)
For Allen Atlas Brain Structures, link to the Scalable Brain Atlas structures
(Rembrandt and Stephen)
Add each parcellation scheme to the Neurolex (can some major ones be added
by Kobe?) (Stephen, Alan, Maryann, Doug, David VE, Maryann, Jyl)
o Determine naming protocol (Alan)
o Properties of each parcellation need to be defined (e.g. defining criteria)
(perhaps Mihail, Doug, David VE, Maryann)
o Ensure synonyms and equivalencies are recognized (BAMS and BrainInfo
have much of this information)
Tie different nomenclature parcellations to the NeuroLex consensus structures
and preferably to an atlas. Get some started before Kobe, a year and beyond
(BAMS, BrainInfo, David VE, Menno)
Recommend best practices for new brain parcellations (begin after Kobe)
o Protocol
o Best practices
Define all properties associated with Brain Regions in NeuroLex
o Ensure that connectivity property can be expressed in terms of neuron to
neuron connectivity property from NRTF
Create OWL representation of BAMS (Alan and Mihail)
Investigate Scalable Brain Atlas (SBA) copyright issues with (Paxinos) (INCF, Gully)
Investigate legal issues of getting Swanson into SBA (Mihail)
Sums DB atlases/parcellations (human, macaque, possibly rat and mouse) into
SBA (David VE, Rembrandt, Stephen)
Sums DB mouse atlas mapped to WHS (David VE, atlasing)
Issues for RDTF:
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Help determine the workflow and coordination among resources: What should
go where? Content will be exposed through Neurolex Wiki, but we have
multiple resources with representations that should be populated.
o NeuroLex
o BAMS
o FMA
o Brain Info
o etc.
Who will define the higher level “data types”, e.g., populations of cells, that will
be necessary for some of the cross scale anatomy?
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Cell component from NIF is being submitted to GO (Chris Mungall is submitting
them and NIF ID is becoming secondary ID). NIF has to maintain a mapping.
o Recommendation: GO should maintain them as secondary IDs. A process
needs to be put into place to handle this.
We will need multiple models from RDTH for how to formalize:
o brain region (above)
o a cell
o a population of cells
o Owl file is standard, but set workflow for syncing to other infrastructure
o also develop recommendations for how others might extend this
Outline set of properties so that inferences can be made across scales
o e.g. brain region X projects to brain region Y if principle neuron with
soma or dendrites in brain region X contacts cell with part of cell in brain
region Y
Put the example cell created by Sean and Giorgio into OWL
Split to work more with the other TFs with occasional sessions within the group
Determine if meeting in Kobe for review of best practices for hierarchies
Infrastructure:
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Create nomenclature pages
Create disambiguation pages (so people can see synonyms while entering) e.g.
NeuroLex wiki: high priority, but easy to do via curation (do you mean this, this,
this, or this)
Define wiki and OBO foundry ontology process for interaction and what needs to
be embedded in NeuroLex. How to define things in terms of the other
ontologies and to import mechanisms and just the pieces we need. Define some
processes and tools. (Alan, David OS, Maryann, Stephen), first pass cell
component with GO, should be a process by Kobe (and hopefully some level of
automation). Alan and David OS will start discussing this first soon
Determine Scalable brain atlas (SBA) issues with Paxinos copyright (Rolf,
Rembrandt, INCF and Gully).
NeuroLex images of structures need to be linked to the images
o mouse: ABA images via SBA
o macaque and human: via Webcarat and SBA (probably faster)
o rat and mouse hippocampus: Menno
o look into Google 3D plug-in and 3D PDF (Stephen)
Recommend best practices for a common model to be used for the major atlas
hierarchies (how to join together anatomical parts; this is how it should be
expressed, OWL or OBO and use certain sets of relations, etc.). May not have
this in place by Kobe, but could have a start and then work while there
Services for accessing ontology
3 months to start pushing data between NeuroLex and Owl (BAMS and FMA)
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